A concise second-order complexity analysis for unconstrained optimization using high-order regularized models
نویسندگان
چکیده
منابع مشابه
Improved second-order evaluation complexity for unconstrained nonlinear optimization using high-order regularized models
The unconstrained minimization of a sufficiently smooth objective function f(x) is considered, for which derivatives up to order p, p ≥ 2, are assumed to be available. An adaptive regularization algorithm is proposed that uses Taylor models of the objective of order p and that is guaranteed to find a firstand second-order critical point in at most O (
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ژورنال
عنوان ژورنال: Optimization Methods and Software
سال: 2019
ISSN: 1055-6788,1029-4937
DOI: 10.1080/10556788.2019.1678033